Pre-processing of the data

Exploring data

Batch effect correction

Clust on Eigen GENES

## Warning: Missing column names filled in: 'X1' [1]
## 
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   .default = col_double(),
##   X1 = col_character()
## )
## ℹ Use `spec()` for the full column specifications.

Deconvolution data

XCell

CIBERSORT

quanTIseq

MCP counter

Differential gene expression analysis

## Unlist done
## Labeling done
## Filtering done
## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors
## Design done
## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors
## Warning: Setting row names on a tibble is deprecated.
## vsd symbols done
## using pre-existing size factors
## estimating dispersions
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
## fitting model and testing
## -- replacing outliers and refitting for 1732 genes
## -- DESeq argument 'minReplicatesForReplace' = 7 
## -- original counts are preserved in counts(dds)
## estimating dispersions
## fitting model and testing
## DESeq done
## using 'normal' for LFC shrinkage, the Normal prior from Love et al (2014).
## 
## Note that type='apeglm' and type='ashr' have shown to have less bias than type='normal'.
## See ?lfcShrink for more details on shrinkage type, and the DESeq2 vignette.
## Reference: https://doi.org/10.1093/bioinformatics/bty895
## res symbols done
## list done

DE results

## Pathway enrichment analysis fGSEA CANARY Good prognosis (G) is the reference. When sample is P, pathways shown below are up- or down- regulated

## `summarise()` ungrouping output (override with `.groups` argument)
## Warning in fgsea(pathways = gmtPathways(pthw_path), stats = ranks, nperm
## = 1000): You are trying to run fgseaSimple. It is recommended to use
## fgseaMultilevel. To run fgseaMultilevel, you need to remove the nperm argument
## in the fgsea function call.
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.01% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## Warning in fgsea(pathways = gmtPathways(pthw_path), stats = ranks, nperm
## = 1000): You are trying to run fgseaSimple. It is recommended to use
## fgseaMultilevel. To run fgseaMultilevel, you need to remove the nperm argument
## in the fgsea function call.
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.01% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## Warning in fgsea(pathways = gmtPathways(pthw_path), stats = ranks, nperm
## = 1000): You are trying to run fgseaSimple. It is recommended to use
## fgseaMultilevel. To run fgseaMultilevel, you need to remove the nperm argument
## in the fgsea function call.
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.01% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## Warning in fgsea(pathways = gmtPathways(pthw_path), stats = ranks, nperm
## = 1000): You are trying to run fgseaSimple. It is recommended to use
## fgseaMultilevel. To run fgseaMultilevel, you need to remove the nperm argument
## in the fgsea function call.
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.01% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## Warning in fgsea(pathways = gmtPathways(pthw_path), stats = ranks, nperm
## = 1000): You are trying to run fgseaSimple. It is recommended to use
## fgseaMultilevel. To run fgseaMultilevel, you need to remove the nperm argument
## in the fgsea function call.
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.01% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## Warning in fgsea(pathways = gmtPathways(pthw_path), stats = ranks, nperm
## = 1000): You are trying to run fgseaSimple. It is recommended to use
## fgseaMultilevel. To run fgseaMultilevel, you need to remove the nperm argument
## in the fgsea function call.
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.01% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## Warning in fgsea(pathways = gmtPathways(pthw_path), stats = ranks, nperm
## = 1000): You are trying to run fgseaSimple. It is recommended to use
## fgseaMultilevel. To run fgseaMultilevel, you need to remove the nperm argument
## in the fgsea function call.
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.01% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## Warning in fgsea(pathways = gmtPathways(pthw_path), stats = ranks, nperm
## = 1000): You are trying to run fgseaSimple. It is recommended to use
## fgseaMultilevel. To run fgseaMultilevel, you need to remove the nperm argument
## in the fgsea function call.
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.01% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## Warning in fgsea(pathways = gmtPathways(pthw_path), stats = ranks, nperm
## = 1000): You are trying to run fgseaSimple. It is recommended to use
## fgseaMultilevel. To run fgseaMultilevel, you need to remove the nperm argument
## in the fgsea function call.
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.01% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.

## # A tibble: 25 x 8
##    pathway                     pval    padj    ES   NES nMoreExtreme  size state
##    <chr>                      <dbl>   <dbl> <dbl> <dbl>        <dbl> <int> <chr>
##  1 HALLMARK_G2M_CHECKPOINT  0.00157 0.00622 0.667  3.16            0   187 up   
##  2 HALLMARK_E2F_TARGETS     0.00156 0.00622 0.618  2.94            0   191 up   
##  3 HALLMARK_INTERFERON_GAM… 0.00154 0.00622 0.512  2.44            0   194 up   
##  4 HALLMARK_ALLOGRAFT_REJE… 0.00157 0.00622 0.436  2.06            0   187 up   
##  5 HALLMARK_MITOTIC_SPINDLE 0.00155 0.00622 0.421  2.00            0   195 up   
##  6 HALLMARK_GLYCOLYSIS      0.00157 0.00622 0.406  1.93            0   188 up   
##  7 HALLMARK_MTORC1_SIGNALI… 0.00155 0.00622 0.405  1.93            0   190 up   
##  8 HALLMARK_INTERFERON_ALP… 0.00162 0.00622 0.449  1.92            0    93 up   
##  9 HALLMARK_HYPOXIA         0.00159 0.00622 0.384  1.81            0   180 up   
## 10 HALLMARK_IL6_JAK_STAT3_… 0.00162 0.00622 0.423  1.78            0    82 up   
## # … with 15 more rows

## # A tibble: 30 x 8
##    pathway     pval   padj     ES   NES nMoreExtreme  size state
##    <chr>      <dbl>  <dbl>  <dbl> <dbl>        <dbl> <int> <chr>
##  1 chr7p22  0.00164 0.0173  0.617  2.58            0    85 up   
##  2 chr20q13 0.00152 0.0173  0.487  2.40            0   265 up   
##  3 MT       0.00173 0.0173  0.702  2.39            0    31 up   
##  4 chr5p15  0.00167 0.0173  0.549  2.22            0    71 up   
##  5 chr14q23 0.00167 0.0173  0.530  2.18            0    77 up   
##  6 chr7q22  0.00157 0.0173  0.471  2.16            0   153 up   
##  7 chr1p33  0.00229 0.0173 -0.533 -2.06            0    40 down 
##  8 chr14q22 0.00167 0.0173  0.518  2.04            0    62 up   
##  9 chr18q12 0.00243 0.0173 -0.494 -2.02            0    51 down 
## 10 chr3p22  0.00261 0.0173 -0.427 -2.00            0   118 down 
## # … with 20 more rows

## # A tibble: 30 x 8
##    pathway                      pval   padj    ES   NES nMoreExtreme  size state
##    <chr>                       <dbl>  <dbl> <dbl> <dbl>        <dbl> <int> <chr>
##  1 ROSTY_CERVICAL_CANCER_PR… 0.00166 0.0215 0.821  3.69            0   130 up   
##  2 FLORIO_NEOCORTEX_BASAL_R… 0.00164 0.0215 0.745  3.48            0   171 up   
##  3 SOTIRIOU_BREAST_CANCER_G… 0.00163 0.0215 0.765  3.46            0   138 up   
##  4 WHITEFORD_PEDIATRIC_CANC… 0.00166 0.0215 0.761  3.33            0   108 up   
##  5 KOBAYASHI_EGFR_SIGNALING… 0.00159 0.0215 0.667  3.27            0   239 up   
##  6 DUTERTRE_ESTRADIOL_RESPO… 0.00150 0.0215 0.645  3.26            0   305 up   
##  7 SHEDDEN_LUNG_CANCER_POOR… 0.00146 0.0215 0.621  3.24            0   426 up   
##  8 LEE_EARLY_T_LYMPHOCYTE_UP 0.00169 0.0215 0.758  3.24            0    95 up   
##  9 CROONQUIST_IL6_DEPRIVATI… 0.00172 0.0215 0.765  3.24            0    91 up   
## 10 ZHOU_CELL_CYCLE_GENES_IN… 0.00165 0.0215 0.727  3.22            0   116 up   
## # … with 20 more rows

## # A tibble: 0 x 8
## # … with 8 variables: pathway <chr>, pval <dbl>, padj <dbl>, ES <dbl>,
## #   NES <dbl>, nMoreExtreme <dbl>, size <int>, state <chr>

## # A tibble: 30 x 8
##    pathway       pval   padj    ES   NES nMoreExtreme  size state
##    <chr>        <dbl>  <dbl> <dbl> <dbl>        <dbl> <int> <chr>
##  1 GNF2_CCNA2 0.00167 0.0104 0.851  3.42            0    65 up   
##  2 GNF2_CDC2  0.00167 0.0104 0.864  3.41            0    59 up   
##  3 GNF2_CCNB2 0.00166 0.0104 0.872  3.38            0    54 up   
##  4 GNF2_CDC20 0.00166 0.0104 0.868  3.34            0    53 up   
##  5 GNF2_CENPF 0.00167 0.0104 0.842  3.32            0    58 up   
##  6 GNF2_HMMR  0.00169 0.0104 0.879  3.28            0    46 up   
##  7 GNF2_PCNA  0.00167 0.0104 0.816  3.28            0    65 up   
##  8 GNF2_MCM4  0.00166 0.0104 0.849  3.26            0    52 up   
##  9 MODULE_54  0.00150 0.0104 0.658  3.21            0   241 up   
## 10 GNF2_RRM1  0.00171 0.0104 0.754  3.19            0    86 up   
## # … with 20 more rows

## # A tibble: 30 x 8
##    pathway                      pval   padj    ES   NES nMoreExtreme  size state
##    <chr>                       <dbl>  <dbl> <dbl> <dbl>        <dbl> <int> <chr>
##  1 GO_DNA_DEPENDENT_DNA_REP… 0.00162 0.0408 0.606  2.78            0   138 up   
##  2 GO_MITOTIC_SISTER_CHROMA… 0.00161 0.0408 0.598  2.75            0   142 up   
##  3 GO_SISTER_CHROMATID_SEGR… 0.00158 0.0408 0.582  2.74            0   171 up   
##  4 GO_CELL_CYCLE_DNA_REPLIC… 0.00172 0.0408 0.684  2.72            0    61 up   
##  5 GO_METAPHASE_PLATE_CONGR… 0.00180 0.0408 0.699  2.71            0    54 up   
##  6 GO_MITOTIC_METAPHASE_PLA… 0.00179 0.0408 0.719  2.68            0    43 up   
##  7 GO_CONDENSED_CHROMOSOME_… 0.00168 0.0408 0.613  2.68            0   102 up   
##  8 GO_DNA_REPLICATION        0.00151 0.0408 0.527  2.59            0   250 up   
##  9 GO_CHROMOSOME_LOCALIZATI… 0.00174 0.0408 0.643  2.58            0    69 up   
## 10 GO_REPLICATION_FORK       0.00176 0.0408 0.645  2.57            0    64 up   
## # … with 20 more rows

## # A tibble: 30 x 8
##    pathway                      pval   padj    ES   NES nMoreExtreme  size state
##    <chr>                       <dbl>  <dbl> <dbl> <dbl>        <dbl> <int> <chr>
##  1 RB_P107_DN.V1_UP          0.00166 0.0176 0.530  2.41            0   128 up   
##  2 CSR_LATE_UP.V1_UP         0.00163 0.0176 0.440  2.06            0   156 up   
##  3 PRC2_EED_UP.V1_DN         0.00166 0.0176 0.429  2.03            0   179 up   
##  4 ATF2_UP.V1_UP             0.00164 0.0176 0.432  2.03            0   165 up   
##  5 KRAS.LUNG.BREAST_UP.V1_UP 0.00162 0.0176 0.418  1.90            0   124 up   
##  6 BMI1_DN_MEL18_DN.V1_UP    0.00166 0.0176 0.413  1.89            0   135 up   
##  7 PRC2_EZH2_UP.V1_DN        0.00165 0.0176 0.402  1.89            0   173 up   
##  8 KRAS.300_UP.V1_DN         0.00166 0.0176 0.415  1.88            0   128 up   
##  9 GCNP_SHH_UP_LATE.V1_UP    0.00164 0.0176 0.394  1.84            0   164 up   
## 10 ATF2_S_UP.V1_UP           0.00164 0.0176 0.392  1.84            0   169 up   
## # … with 20 more rows

## # A tibble: 30 x 8
##    pathway                      pval   padj    ES   NES nMoreExtreme  size state
##    <chr>                       <dbl>  <dbl> <dbl> <dbl>        <dbl> <int> <chr>
##  1 GSE15750_DAY6_VS_DAY10_T… 0.00156 0.0205 0.691  3.28            0   188 up   
##  2 GSE15750_DAY6_VS_DAY10_E… 0.00155 0.0205 0.687  3.26            0   181 up   
##  3 GSE21063_WT_VS_NFATC1_KO… 0.00154 0.0205 0.659  3.13            0   178 up   
##  4 GSE24634_TEFF_VS_TCONV_D… 0.00156 0.0205 0.658  3.12            0   192 up   
##  5 GSE14415_NATURAL_TREG_VS… 0.00159 0.0205 0.660  3.10            0   173 up   
##  6 GSE27241_WT_VS_RORGT_KO_… 0.00162 0.0205 0.670  3.06            0   142 up   
##  7 GSE39556_CD8A_DC_VS_NK_C… 0.00156 0.0205 0.640  3.03            0   185 up   
##  8 GSE13547_CTRL_VS_ANTI_IG… 0.00157 0.0205 0.640  3.01            0   174 up   
##  9 GSE30962_PRIMARY_VS_SECO… 0.00157 0.0205 0.628  2.97            0   184 up   
## 10 GSE39110_DAY3_VS_DAY6_PO… 0.00155 0.0205 0.625  2.96            0   183 up   
## # … with 20 more rows

## # A tibble: 30 x 8
##    pathway                      pval   padj    ES   NES nMoreExtreme  size state
##    <chr>                       <dbl>  <dbl> <dbl> <dbl>        <dbl> <int> <chr>
##  1 ROSTY_CERVICAL_CANCER_PR… 0.00174 0.0283 0.821  3.77            0   130 up   
##  2 SOTIRIOU_BREAST_CANCER_G… 0.00174 0.0283 0.765  3.55            0   138 up   
##  3 FLORIO_NEOCORTEX_BASAL_R… 0.00169 0.0283 0.745  3.53            0   171 up   
##  4 GNF2_CDC2                 0.00179 0.0283 0.864  3.48            0    59 up   
##  5 GNF2_CCNA2                0.00181 0.0283 0.851  3.47            0    65 up   
##  6 GNF2_CCNB2                0.00181 0.0283 0.872  3.42            0    54 up   
##  7 GNF2_CDC20                0.00181 0.0283 0.868  3.39            0    53 up   
##  8 WHITEFORD_PEDIATRIC_CANC… 0.00177 0.0283 0.761  3.39            0   108 up   
##  9 GNF2_CENPF                0.00181 0.0283 0.842  3.36            0    58 up   
## 10 GNF2_HMMR                 0.00181 0.0283 0.879  3.34            0    46 up   
## # … with 20 more rows